


'''simple docstring'''


import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


UpperCAmelCase_						:  Dict				  =						{
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

UpperCAmelCase_						:  Tuple				  =						{
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= list(state_dict.keys()	)
				for name in state_dict_keys:
								_SCREAMING_SNAKE_CASE					:			Tuple							= state_dict.pop(SCREAMING_SNAKE_CASE__	)
								# emb -> embedding
								if name.startswith("""emb."""	):
												_SCREAMING_SNAKE_CASE					:			Any							= name.replace("""emb."""				, """embeddings."""	)
								# ln_0 -> pre_ln (only present at block 0)
								if name.startswith("""blocks.0.ln0"""	):
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= name.replace("""blocks.0.ln0"""				, """blocks.0.pre_ln"""	)
								# att -> attention
								_SCREAMING_SNAKE_CASE					:			Any							= re.sub(R"""blocks\.(\d+)\.att"""				, R"""blocks.\1.attention"""				, SCREAMING_SNAKE_CASE__	)
								# ffn -> feed_forward
								_SCREAMING_SNAKE_CASE					:			int							= re.sub(R"""blocks\.(\d+)\.ffn"""				, R"""blocks.\1.feed_forward"""				, SCREAMING_SNAKE_CASE__	)
								# time_mix_k -> time_mix_key and reshape
								if name.endswith(""".time_mix_k"""	):
												_SCREAMING_SNAKE_CASE					:			Dict							= name.replace(""".time_mix_k"""				, """.time_mix_key"""	)
								# time_mix_v -> time_mix_value and reshape
								if name.endswith(""".time_mix_v"""	):
												_SCREAMING_SNAKE_CASE					:			List[str]							= name.replace(""".time_mix_v"""				, """.time_mix_value"""	)
								# time_mix_r -> time_mix_key and reshape
								if name.endswith(""".time_mix_r"""	):
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= name.replace(""".time_mix_r"""				, """.time_mix_receptance"""	)

								if name != "head.weight":
												_SCREAMING_SNAKE_CASE					:			Any							= """rwkv.""" + name

								_SCREAMING_SNAKE_CASE					:			Dict							= weight
				return state_dict







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=None				, SCREAMING_SNAKE_CASE__=False				, SCREAMING_SNAKE_CASE__=None	):







				"""simple docstring"""





				if tokenizer_file is None:
								print("""No `--tokenizer_file` provided, we will use the default tokenizer."""	)
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= 5_0277
								_SCREAMING_SNAKE_CASE					:			Dict							= AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""	)
				else:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= len(SCREAMING_SNAKE_CASE__	)
				tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)

				# 2. Build the config
				_SCREAMING_SNAKE_CASE					:			List[str]							= list(NUM_HIDDEN_LAYERS_MAPPING.keys()	)
				if size is None:
								# Try to infer size from the checkpoint name
								for candidate in possible_sizes:
												if candidate in checkpoint_file:
																_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= candidate
																break
								if size is None:
												raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""	)
				if size not in possible_sizes:
								raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}."""	)

				_SCREAMING_SNAKE_CASE					:			Tuple							= RwkvConfig(
				    vocab_size=SCREAMING_SNAKE_CASE__				, num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				, hidden_size=HIDEN_SIZE_MAPPING[size]				, )
				config.save_pretrained(SCREAMING_SNAKE_CASE__	)

				# 3. Download model file then convert state_dict
				_SCREAMING_SNAKE_CASE					:			List[Any]							= hf_hub_download(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.load(SCREAMING_SNAKE_CASE__				, map_location="""cpu"""	)
				_SCREAMING_SNAKE_CASE					:			int							= convert_state_dict(SCREAMING_SNAKE_CASE__	)

				# 4. Split in shards and save
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= shard_checkpoint(SCREAMING_SNAKE_CASE__	)
				for shard_file, shard in shards.items():
								torch.save(SCREAMING_SNAKE_CASE__				, os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)

				if index is not None:
								_SCREAMING_SNAKE_CASE					:			Any							= os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
								# Save the index as well
								with open(SCREAMING_SNAKE_CASE__				, """w"""				, encoding="""utf-8"""	) as f:
												_SCREAMING_SNAKE_CASE					:			List[str]							= json.dumps(SCREAMING_SNAKE_CASE__				, indent=2				, sort_keys=SCREAMING_SNAKE_CASE__	) + """\n"""
												f.write(SCREAMING_SNAKE_CASE__	)

								# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
								print(
								    """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""	)
								_SCREAMING_SNAKE_CASE					:			Dict							= list(shards.keys()	)

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												_SCREAMING_SNAKE_CASE					:			Any							= torch.load(os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)
												torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				, os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)	)

				del state_dict
				gc.collect()

				if push_to_hub:
								if model_name is None:
												raise ValueError("""Please provide a `model_name` to push the model to the Hub."""	)
								_SCREAMING_SNAKE_CASE					:			int							= AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__	)
								model.push_to_hub(SCREAMING_SNAKE_CASE__				, max_shard_size="""2GB"""	)
								tokenizer.push_to_hub(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  Optional[int]				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
				)
				parser.add_argument(
				    '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
				)
				parser.add_argument(
				    '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
				)
				parser.add_argument(
				    '--tokenizer_file',
				    default=None,
				    type=str,
				    help='Path to the tokenizer file to use (if not provided, only the model is converted).',
				)
				parser.add_argument(
				    '--size',
				    default=None,
				    type=str,
				    help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    action='store_true',
				    help='Push to the Hub the converted model.',
				)
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help='Name of the pushed model on the Hub, including the username / organization.',
				)

				UpperCAmelCase_						:  Union[str, Any]				  =						parser.parse_args()
				convert_rmkv_checkpoint_to_hf_format(
				    args.repo_id,
				    args.checkpoint_file,
				    args.output_dir,
				    size=args.size,
				    tokenizer_file=args.tokenizer_file,
				    push_to_hub=args.push_to_hub,
				    model_name=args.model_name,
				)








'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) <= 1 or n <= 1:
								return

				insert_next(SCREAMING_SNAKE_CASE__				, n - 1	)
				rec_insertion_sort(SCREAMING_SNAKE_CASE__				, n - 1	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if index >= len(SCREAMING_SNAKE_CASE__	) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(SCREAMING_SNAKE_CASE__				, index + 1	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						input('Enter integers separated by spaces: ')
				UpperCAmelCase_						:  list[int]				  =						[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)




